We speak with Jason Loh, Advanced Analytics Practice Lead at SAS to learn about the learn about the most innovative AI and machine learning implementations today

Artificial intelligence (AI) and machine learning (ML) are two of the hottest technologies today due to their huge potential to derive new insights and value out of data. According to IDC, machine learning and artificial intelligence will produce 30 - 40 per cent in cost savings and productivity improvements in operations management by 2020.

These advantages have sparked both intense interest as well as concern amongst organisations, given that late adopters will struggle to remain competitive. Yet whilst organisations are eager to harness its potential, at present, this remains a somewhat futuristic concept for most. Few projects have been deployed at scale and hype has not helped either, spreading confusion about how well the technology works and what it can actually do.

To demystify these technologies and learn about the most innovative technology implementations today, I spoke with one of the leading experts in the field: Jason Loh, Analytics Practice Lead, SAS AP.

Image: Jason Loh, Analytics Practice Lead, SAS AP

Demystifying AI and ML

For non-experts, one of the first sources of confusion is often the various terminologies surrounding ML and AI. But it's not just the subtleties in their meaning that is confusing, explains Jason, often these terms are used interchangeably. "If you talk about the common academic definition of machine learning - it simply describes the task of having a machine, usually referring to computers, to perform learning, referring to extracting patterns from data that describe behaviour or events, to achieve a specific outcome. A common example would be offering product recommendations based on previous customer reviews"

Deep learning, another term you have likely heard mentioned, is a very specific class on ML. This refers to ML based on deep neural networks, which means, having many layers performing learning in multiple steps. Image recognition is often performed this way, by processing a hierarchy of features where each layer looks for more complicated objects. For example, when auto-tagging friends in photos, the first layer of a deep network might be trained to detect facial features like the relative position of the eyes, noses and cheekbones, with other layers recognizing hair and eye colour, as well as who are your closest friends and the people you tag most regularly.

Out of all terms related to ML, Jason is keen to emphasise one in particular: analytics. "The popularity of terms like AI and ML have risen and fallen over the years, but the word analytics brings an additional meaning, focusing on what is going on behind the data." He argues that whilst ML may perform a task and deliver a decision, users sometimes do not understand how the decision was arrived at, which can lead to problems for the organisation.